A lightweight vision transformer with context-aware convolution and uniformity normalization for Alzheimer's Disease...

Early and accurate diagnosis of Alzheimer's Disease (AD) is crucial for effective clinical intervention.

In this study, we propose a lightweight vision transformer architecture specifically designed for AD classification using 2D brain MRI slices. LICAUN-ViT incorporates three key innovations: Mono-Head Self-Attention (MOHSA) to reduce computational overhead, Uniformity Normalization (Uni-Norm) to mitigate oversmoothing and enhance feature diversity, and Context-Aware Convolution (CAC) to integrate long-range dependencies with local structural features.

Evaluated on two benchmark datasets derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our model achieves state-of-the-art performance with an accuracy of 93.03 % on axial slices and 94.15 % on sagittal slices, while maintaining relatively low floating-point operations (FLOPs) for efficient deployment. Extensive ablation studies and singular value analyses confirm the effectiveness and robustness of the proposed components.

These results demonstrate that the proposed model offers a computationally efficient and promising solution for automated AD diagnosis, with strong potential for clinical integration.